• NeuroImage. Clinical · Jan 2018

    Quantitative analysis of neurite orientation dispersion and density imaging in grading gliomas and detecting IDH-1 gene mutation status.

    • Jing Zhao, Ji-Bin Li, Jing-Yan Wang, Yu-Liang Wang, Da-Wei Liu, Xin-Bei Li, Yu-Kun Song, Yi-Su Tian, Xu Yan, Zhu-Hao Li, Shao-Fu He, Xiao-Long Huang, Li Jiang, Zhi-Yun Yang, and Jian-Ping Chu.
    • Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, Guangdong 510080, China.
    • Neuroimage Clin. 2018 Jan 1; 19: 174-181.

    Background And PurposeNeurite orientation dispersion and density imaging (NODDI) is a new diffusion MRI technique that has rarely been applied for glioma grading. The purpose of this study was to quantitatively evaluate the diagnostic efficiency of NODDI in tumour parenchyma (TP) and peritumoural area (PT) for grading gliomas and detecting isocitrate dehydrogenase-1 (IDH-1) mutation status.MethodsForty-two patients (male: 23, female: 19, mean age: 44.5 y) were recruited and underwent whole brain NODDI examination. Intracellular volume fraction (icvf) and orientation dispersion index (ODI) maps were derived. Three ROIs were manually placed on TP and PT regions for each case. The corresponding average values of icvf and ODI were calculated, and their diagnostic efficiency was assessed.ResultsTumours with high icvfTP (≥0.306) and low icvfPT (≤0.331) were more likely to be high-grade gliomas (HGGs), while lesions with low icvfTP (<0.306) and high icvfPT (>0.331) were prone to be low-grade gliomas (LGGs) (P < 0.001). A multivariate logistic regression model including patient age and icvf values in TP and PT regions most accurately predicted glioma grade (AUC = 0.92, P < 0.001), with a sensitivity and specificity of 92% and 89%, respectively. However, no significant differences were found in NODDI metrics for differentiating IDH-1 mutation status.ConclusionsThe quantitative NODDI metrics in the TP and PT regions are highly valuable for glioma grading. A multivariate logistic regression model using the patient age and the icvf values in TP and PT regions showed very high predictive power. However, the utility of NODDI metrics for detecting IDH-1 mutation status has not been fully explored, as a larger sample size may be necessary to uncover benefits.

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